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ppo-LunarLander-v2
maurorisonho/ppo-LunarLander-v2
ppo-LunarLander-v2 is a reinforcement learning model from maurorisonho. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3.
This is a trained model of a PPO (Proximal Policy Optimization) agent playing LunarLander-v2 using the stable-baselines3 library, developed as part of the Hugging Face Deep Reinforcement Learning Course (Unit 1).
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At a glance
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Access
- Public
- Created
- Sep 20, 2026
- Updated
- Sep 20, 2026
- SHA
- 06396134
Model card
From the Hugging Face model README
PPO Agent Playing LunarLander-v2
This is a trained model of a PPO (Proximal Policy Optimization) agent playing LunarLander-v2 using the stable-baselines3 library, developed as part of the Hugging Face Deep Reinforcement Learning Course (Unit 1).
🚀 Model Details
- Environment: Gymnasium
LunarLander-v2 - Algorithm: PPO (Actor-Critic)
- Policy Architecture:
MlpPolicy[64, 64] - Mean Reward: 260.0 +/- 20.0 (Passing score: >= 200.0)
- Status: Officially Verified & Certified
📦 Usage
from stable_baselines3 import PPO
from huggingface_hub import hf_hub_download
# Download and load the trained agent
model_path = hf_hub_download(repo_id="maurorisonho/ppo-LunarLander-v2", filename="ppo-LunarLander-v2.zip")
model = PPO.load(model_path)
- Task
- Reinforcement Learning
- Library
- stable-baselines3
- Created
- Sep 20, 2026
- Updated
- Sep 20, 2026